---
title: "Ecommerce Leads"
output:
flexdashboard::flex_dashboard:
theme: spacelab
vertical_layout: fill
source_code: embed
logo: "/Users/shanicetan/Desktop/Desktop/Data/netsuite.png"
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(readxl)
library(plotly)
library(plyr)
library(dplyr)
library(tidyverse)
library(DT)
library(RColorBrewer)
#Import ecommerce excel sheet
ecommerce <- "/Users/shanicetan/Desktop/Desktop/Data/singaporecommerce.xlsx" %>% excel_sheets() %>% set_names() %>% map(read_excel, path = "/Users/shanicetan/Desktop/Desktop/Data/singaporecommerce.xlsx")
n <- length(ecommerce)
```
Q3 {data-navmenu="2019"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e1 <- ecommerce[[n]][,2:15, drop=FALSE]
e1 <- as.data.frame(e1)
colnames(e1)[1] <- "Merchant"
e1 <- e1 %>%
filter(!location == 'international')
e1 <- tail(e1,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r }
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e1 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e1, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7, type = 'bar', text = ~category, hoverinfo = "text+x") %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e1dt = subset(e1, select = -c(ios,android,twitter,instagram,facebook,logodesktop))
datatable(e1dt, rownames = FALSE)
```
### Pie chart of categories
```{r}
pie1 <- e1 %>%
group_by(category) %>%
summarise(count = n()) %>%
plot_ly(labels = ~category,
values = ~count,
textposition = "inside",
textinfo = "percent+label",
insidetextorientation='horizontal',
marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2","E6E6FA"))) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F), showlegend=FALSE, annotations=list(text="Quarterly Top Categories","showarrow"=F))
pie1
```
Q2 {data-navmenu="2019"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e2 <- ecommerce[[n-1]][,2:14, drop=FALSE]
e2 <- as.data.frame(e2)
colnames(e2)[1] <- "Merchant"
e2 <- e2 %>%
filter(!location == 'international')
e2 <- tail(e2,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r }
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e2 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e2, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7, type = 'bar', text = ~category, hoverinfo = "text+x") %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e2dt = subset(e2, select = -c(ios,android,twitter,instagram,facebook,logodesktop))
datatable(e2dt, rownames = FALSE)
```
### Pie chart of categories
```{r}
pie2 <- e2 %>%
group_by(category) %>%
summarise(count = n()) %>%
plot_ly(labels = ~category,
values = ~count,
textposition = "inside",
textinfo = "percent+label",
insidetextorientation='horizontal',
marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2","E6E6FA"))) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F), showlegend=FALSE, annotations=list(text="Quarterly Top Categories","showarrow"=F))
pie2
```
Q1 {data-navmenu="2019"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e3 <- ecommerce[[n-2]][,2:14, drop=FALSE]
e3 <- as.data.frame(e3)
colnames(e3)[1] <- "Merchant"
e3 <- e3 %>%
filter(!location == 'international')
e3 <- tail(e3,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r }
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e3 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e3, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7, type = 'bar', text = ~category, hoverinfo = "text+x") %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e3dt = subset(e3, select = -c(`iOS rank`,`Android rank`,twitter,instagram,facebook,logodesktop))
datatable(e3dt, rownames = FALSE)
```
### Pie chart of categories
```{r}
pie3 <- e3 %>%
group_by(category) %>%
summarise(count = n()) %>%
plot_ly(labels = ~category,
values = ~count,
textposition = "inside",
textinfo = "percent+label",
insidetextorientation='horizontal',
marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2","E6E6FA"))) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F), showlegend=FALSE, annotations=list(text="Quarterly Top Categories","showarrow"=F))
pie3
```
Q4 {data-navmenu="2018"}
===========================================
```{r, include=FALSE}
#remove international merchants and top 3 players
e4 <- ecommerce[[n-3]][,2:14, drop=FALSE]
e4 <- as.data.frame(e4)
colnames(e4)[1] <- "Merchant"
e4 <- e4 %>%
filter(!location == 'international')
e4 <- tail(e4,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r }
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e4 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e4, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7, type = 'bar', text = ~category, hoverinfo = "text+x") %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e4dt = subset(e4, select = -c(`iOS rank`,`Android rank`,twitter,instagram,facebook,logodesktop))
datatable(e4dt, rownames = FALSE)
```
### Pie chart of categories
```{r}
pie4 <- e4 %>%
group_by(category) %>%
summarise(count = n()) %>%
plot_ly(labels = ~category,
values = ~count,
textposition = "inside",
textinfo = "percent+label",
insidetextorientation='horizontal',
marker = list(colors = c("#A9CCE3","#A3E4D7","#D7BDE2","E6E6FA"))) %>%
add_pie(hole = 0.5) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F), showlegend=FALSE, annotations=list(text="Quarterly Top Categories","showarrow"=F))
pie4
```
Q3 {data-navmenu="2018"}
===========================================
```{r, include=FALSE}
#remove international merchants and top 3 players
e5 <- ecommerce[[n-4]][,2:14, drop=FALSE]
e5 <- as.data.frame(e5)
colnames(e5)[1] <- "Merchant"
e5 <- tail(e5,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r}
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e5 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e5, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7) %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e5dt = subset(e5, select = -c(`iOS rank`,`Android rank`,Twitter,Instagram,Facebook,Source...5,Source...9,Source...11,Source...13))
datatable(e5dt, rownames = FALSE)
```
Q2 {data-navmenu="2018"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e6 <- ecommerce[[n-5]][,2:14, drop=FALSE]
e6 <- as.data.frame(e6)
colnames(e6)[1] <- "Merchant"
e6 <- tail(e6,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r }
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e6 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e6, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7) %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e6dt = subset(e6, select = -c(`iOS rank`,`Android rank`,Twitter,Instagram,Facebook,Source...5,Source...9,Source...11,Source...13))
datatable(e6dt, rownames = FALSE)
```
Q1 {data-navmenu="2018"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e7 <- ecommerce[[n-6]][,2:14, drop=FALSE]
e7 <- as.data.frame(e7)
colnames(e7)[1] <- "Merchant"
e7 <- tail(e7,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r}
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e7 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e7, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7) %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e7dt = subset(e7, select = -c(`iOS rank`,`Android rank`,Twitter,Instagram,Facebook,Source...5,Source...9,Source...11,Source...13))
datatable(e7dt, rownames = FALSE)
```
Q4 {data-navmenu="2017"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e8 <- ecommerce[[n-7]][,2:14, drop=FALSE]
e8 <- as.data.frame(e8)
colnames(e8)[1] <- "Merchant"
e8 <- tail(e8,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r}
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e8 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e8, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7) %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e8dt = subset(e8, select = -c(`iOS rank`,`Android rank`,Twitter,Instagram,Facebook,Source...5,Source...9,Source...11,Source...13))
datatable(e8dt, rownames = FALSE)
```
Q3 {data-navmenu="2017"}
===========================================
```{r , include=FALSE}
#remove international merchants and top 3 players
e9 <- ecommerce[[n-8]][,2:14, drop=FALSE]
e9 <- as.data.frame(e9)
colnames(e9)[1] <- "Merchant"
e9 <- tail(e9,-3)
```
Column {.tabset .tabset-fade}
-----------------------------------
### Top Ecommerce Merchants
```{r}
#Group Harvard dataframe by Merchant and their respective counts
p1 <- e9 %>%
group_by(Merchant) %>%
summarise(count = traffic)
p1 <- left_join(p1, e9, by = "Merchant")
#Rank in bars ascending order of counts
p1$Merchant <- factor(p1$Merchant, levels = unique(p1$Merchant)[order(p1$count, decreasing = FALSE)])
#Plot bar chart of Merchants based on popularity
plot_ly(p1, x = ~count, y = ~Merchant, color = ~Merchant, colors = "GnBu", alpha = 0.7) %>% layout(yaxis = list(title = 'Merchant'), xaxis = list(title = "Site Traffic"))
```
### Datatable
```{r}
e9dt = subset(e9, select = -c(Twitter,Instagram,Facebook,Source...5,Source...9,Source...11,Source...13))
datatable(e9dt, rownames = FALSE)
```
